Markovian Dynamics in Chromatin Loop Extrusion Factors
Bibliographic record
Abstract
Markov properties can be used to model different dynamic processes at various stages of the loop extrusion process. The current methods are proposed to gain insight on how Markov models may illustrate chromatin behaviour once the appropriate observed data becomes available. We find that single molecule FRET experiments are able to identify the conformational states chromatin using Gaussian mixture models. The unbinding and binding rates of loop extrusion factors (LEFs) were applied in an immigration-death model, and found to play a role in influencing the frequency of loop extrusion. By including the additional parameter of the presence of nucleosomes with LEF binding on a strand of DNA, we find that the theoretical timescale of DNA exposure decreased upon LEF binding. The binding behaviour of LEFs is also dependent on the location of nucleosomes on a strand of DNA. This is modeled with the Gillespie algorithm to simulate LEF binding activity with single cell dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".